Utility of second derivative of the finger photoplethysmogram for the estimation of the risk of coronary heart disease in the general population

Vascular dysfunction in women with recurrent pregnancy loss: Possible association with antiphospholipid antibodies

OBJECTIVE

Antiphospholipid antibodies (aPL) are recognized to have a pivotal role in recurrent pregnancy loss (RPL) and cardiovascular disease. Therefore, we assessed the vascular function of women with RPL and examined the association with each type of aPL.

METHODS

In this retrospective study, 569 women with RPL and 55 healthy women who had never experienced pregnancy loss were recruited. We performed blood tests for aPL and acceleration plethysmography (APG) to evaluate peripheral vascular function.

RESULTS

The differential pulse wave index (DPI), indicating vascular elasticity, was significantly lower in women with RPL (115.6 ± 4.1) compared to the control group (117.0 ± 2.3). DPI in RPL women with anti-β2 glycoprotein I (aβ2GPI) IgG was significantly lower than those without. Remained blood volume (RBV), indicating post-vasoconstriction blood content, was significantly higher in RPL women with aβ2GI IgG than in those without. Regression analysis showed aβ2GPI IgG and body mass index (BMI) linked negatively with DPI.

CONCLUSION

Women with RPL have subclinical vascular dysfunction even at reproductive age. It is possible that aβ2GPI IgG is associated with vascular dysfunction in RPL women.

Alterations in cerebral artery flow velocity acceleration pattern correlate with cognitive impairment in diabetes mellitus

INTRODUCTION

Flow velocity acceleration pattern is related to shear stress, pressure changes, cardiovascular risk factors, diabetes mellitus, hypertension, endothelial dysfunction and arterial stiffness. Considering the hemodynamic alterations in cognitive impairment, perturbations in cerebral artery flow acceleration pattern may correlate with cognitive impairment, which could enhance our understanding of how cardiovascular risk factors drive cognitive decline from a mechanistic point of view.

METHOD

The first derivative of middle cerebral artery flow velocity waveforms obtained via transcranial Doppler were computed to visualize acceleration/deceleration waves (a,b,c,d,e) in ensemble-averaged signals. Vascular Aging Index was calculated per its definition (VAI:(b-c-d-e)/a). Relationship between multiple cognitive domains and VAI was evaluated with standard statistical tests.

RESULTS

VAI was significantly correlated with HVLT total recall (Hopkins Verbal Learning Test-revised) (r: -0.310 p: 0.046, n: 42), delayed recall (r: -0.396 p: 0.009), % Retention (r: -0.305 p: 0.050) and components of RCFT(Rey-Osterrieth Complex Figure Test), namely raw copy score (r: -0.524 p < 0.001), immediate recall (r: -0.323 p: 0.037). Controlling for age, body mass index, gray matter volume and diabetes duration yielded stronger correlations but lower group numbers due to missing data. Correlation coefficients for VAI with HVLT delayed recall and % Retention were -0.439 (p: 0.012) and -0.444(p: 0.011 n: 36) respectively. Likewise the adjusted correlations of VAI with RCFT components were improved (Raw Copy r: -0.557 p < 0.001, Immediate Recall r: -0.440 p: 0.012, Delayed Recall (r: -0.358 p: 0.044).

CONCLUSION

In patients with diabetes, cerebral artery flow velocity acceleration pattern correlates with cognitive performance in visuo-constructional and verbal cognitive domains. Computational fluid dynamics may aid developing a better mechanistic understanding of arterial hemodynamics-cortical function coupling.

Second Derivative of the Finger Photoplethysmogram Predicts the Risk of Developing Hypertension in Middle-Aged Men

AIM

Increased arterial stiffness impairs the functional and structural properties of arteries, which in turn elevates blood pressure (BP). The aim of this study was to test whether indices obtained from the second derivative of the finger photoplethysmogram (SDPTG), a marker of arterial stiffness, predict future development of hypertension in middle-aged men.

METHODS

The SDPTG was measured in 902 men without hypertension (mean age 44±6 years) at an annual medical checkup. The development of hypertension was monitored for a maximum of 4 years. Two indices of arterial stiffness were calculated from the SDPTG waveforms: b/a, an index of large elastic arterial stiffness, and d/a, an index of systemic arterial stiffness, including the structural and functional properties of small and muscular arteries and peripheral circulation. A Cox proportional hazards model was used to examine whether the b/a and d/a ratios were independent predictors of future development of hypertension.

RESULTS

During the follow-up period, 124 individuals developed hypertension, defined as a systolic/diastolic BP ≥ 140/90 mm Hg or the use of antihypertensive medications. The hazard ratio for the development of hypertension significantly increased in the lowest quartile of the d/a ratio (2.84, 95% confidence interval: 1.58-5.13, p＜0.001) compared with the highest quartile, after adjusting for multiple potential confounders. In contrast, the b/a ratio did not show significant hazard ratios for the development of hypertension.

CONCLUSIONS

The d/a ratio, calculated from the SDPTG waveforms, predicted the risk of future development of hypertension in this study population.

Photoplethysmography Features Correlated with Blood Pressure Changes

Blood pressure measurement is a key indicator of vascular health and a routine part of medical examinations. Given the ability of photoplethysmography (PPG) signals to provide insights into the microvascular bed and their compatibility with wearable devices, significant research has focused on using PPG signals for blood pressure estimation. This study aimed to identify specific clinical PPG features that vary with different blood pressure levels. Through a literature review of 297 publications, we selected 16 relevant studies and identified key time-dependent PPG features associated with blood pressure prediction. Our analysis highlighted the second derivative of PPG signals, particularly the b/a and d/a ratios, as the most frequently reported and significant predictors of systolic blood pressure. Additionally, features from the velocity and acceleration photoplethysmograms were also notable. In total, 29 features were analyzed, revealing novel temporal domain features that show promise for further research and application in blood pressure estimation.

Data-knowledge co-driven feature based prediction model via photoplethysmography for evaluating blood pressure

BACKGROUND

Knowledge feature (KF) with clear physiological significance of photoplethysmography are widely used in predicting blood pressure. However, KF primarily focus on local information of photoplethysmography, which may struggle to capture the overall characteristics.

METHODS

Firstly, functional data analysis (FDA) was introduced to extract two types of data feature (DF). Furthermore, data-knowledge co-driven feature (DKCF) was proposed by combining FDA and constraints of KF. Finally, random forest, ada boost, gradient boosting, support vector machine and deep neural network were adopted, to compare the abilities of KF, DFs and DKCF in predicting blood pressure with two datasets (A published dataset and a self-collected dataset).

RESULTS

Under the premise of extracting only 9 features, the average mean absolute errors (MAE) of systolic blood pressure (SBP) and diastolic blood pressure (DBP) obtained by DKCF are both the smallest in dataset 1. In dataset 2, DKCF acquires the smallest MAE in predicting SBP and obtains the second smallest MAE in predicting DBP.

CONCLUSIONS

The results demonstrate that low-dimensional DKCF of photoplethysmography is closely correlated with blood pressure, which may serve as an important indicator for health assessment.

Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24 GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14 mmHg and -0.20 ±5.50 mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

Black Soybean Seed Coat Extract Improves Endothelial Function and Upregulates Oxidative Stress Marker Expression in Healthy Volunteers by Stimulating Nitric Oxide Production in Endothelial Cells

Black soybean seed coat extract (BE) contains multiple bioactive polyphenols, including flavan-3-ols and anthocyanins. BE improves endothelial function; however, it is unclear whether BE protects endothelial cells from senescence. In this study, we examined the effects of BE on endothelial cell senescence and vascular function in healthy individuals. High concentrations of glucose were used to induce senescence in bovine aortic endothelial cells incubated with BE. Senescence, vascular function, and oxidative stress markers were measured. Incubation with BE remarkably inhibited senescence-associated β-galactosidase and lactate dehydrogenase activities and dose dependently reduced intracellular reactive oxygen species levels in bovine aortic endothelial cells. BE treatment increased the levels of endothelial nitric oxide synthase (eNOS) mRNA and endothelial nitric oxide (NO) metabolites and increased the mRNA expression of klotho, a gene associated with an antiaging phenotype. To examine the effects of BE in humans, we conducted a clinical study using the second derivative of the fingertip photoplethysmogram to investigate vascular function and aging in 24 healthy volunteers. The participants consumed BE supplements (100 mg/day) or a placebo for 2 weeks. When compared with the placebo group, the BE group showed considerably improved vascular function, NO metabolite levels, and oxidative stress. These results suggest that BE supplementation improves endothelial function, possibly through antioxidant activity and NO production, and may consequently reduce the cardiovascular risk associated with aging. BE supplementation may be an effective and safe approach to reduce the risk of atherosclerosis and cardiovascular disease; however, additional studies investigating chronic vascular inflammation are needed.

Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram

Background

Hypertension is associated with severe complications, and its detection is important to provide early information about a hypertension event, which is essential to prevent further complications.

Objective

This study aimed to investigate a strategy for hypertension detection without a cuff using parameters of bioelectric signals, i.e., Electrocardiogram (ECG), Photoplethysmogram (PPG,) and an algorithm of Swarm-based Support Vector Machine (SSVM).

Material and Methods

This experimental study was conducted to develop a hypertension detection system. ECG and PPG bioelectrical records were collected from the Medical Information Mart for Intensive Care (MIMIC) from normal and hypertension participants and processed to find the parameters, used for the inputs of SSVM and comprised Pulse Arrival Time (PAT) and the characteristics of PPG signal derivatives. The SSVM was n Support Vector Machine (SVM) algorithm optimized using particle swarm optimization with Quantum Delta-potential-well (QDPSO). The SSVMs with different inputs were investigated to find the optimal detection performance.

Results

The proposed strategy was performed at 96% in terms of F1-score, accuracy, sensitivity, and specificity with better performance than the other methods tested and methods and also could develop a cuff-free hypertension monitoring system.

Conclusion

Hypertension using SSVM, ECG, and PPG parameters is acceptably performed. The hypertension detection had lower performance utilizing only PPG than both ECG and PPG.

Association between blood glucose levels and arterial stiffness marker: comparing the second derivative of photoplethysmogram and cardio-ankle vascular index scores

Objective

This study aimed to compare the association between fasting plasma glucose (FPG) and glycosylated hemoglobin A1c (HbA1c) levels using the second derivative of photoplethysmogram (SDPTG) index and the cardio-ankle vascular index (CAVI).

Methods

Electronic medical records of 276 participants (160 men, 116 women) who visited the health promotion center of a university hospital were examined. Age, sex, body mass index (BMI), blood pressure, and lipid profile were considered as risk factors for arterial stiffness, together with the FPG, HbA1c, CAVI, and SDPTG indices. Hierarchical regression models were constructed, and all participants were divided into low-normal, high-normal, prediabetic, and diabetic groups to examine the group-based differences in CAVI and SDPTG indices.

Results

FPG and HbA1c were independently predictive of increased CAVI, and their predictive powers for CAVI were equivalent (β = 0.214 and 0.200, respectively). Risk factors, including age, BMI, and male sex, were also predictive of CAVI (β= 0.593-0.630, -0.256 - -0.280, and 0.142-0.178, respectively). None of the FPG and HbA1c values were predictive of the SDPTG indices. The CAVI was higher in the diabetes group than in the other three groups according to HbA1c level, while the d/a index of the SDPTG decreased in the prediabetes group and increased in the diabetes group.

Conclusions

CAVI may not be substituted for SDPTG indices when evaluating arterial stiffness based on the glucose level. Moreover, the progression rate of arterial stiffness may differ between the diabetic and nondiabetic stages.

Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals

Photoplethysmography (PPG) signals are widely used in clinical practice as a diagnostic tool since PPG is noninvasive and inexpensive. In this article, machine learning techniques were used to improve the performance of classifiers for the detection of cardiovascular disease (CVD) from PPG signals. PPG signals occupy a large amount of memory and, hence, the signals were dimensionally reduced in the initial stage. A total of 41 subjects from the Capno database were analyzed in this study, including 20 CVD cases and 21 normal subjects. PPG signals are sampled at 200 samples per second. Therefore, 144,000 samples per patient are available. Now, a one-second-long PPG signal is considered a segment. There are 720 PPG segments per patient. For a total of 41 subjects, 29,520 segments of PPG signals are analyzed in this study. Five dimensionality reduction techniques, such as heuristic- (ABC-PSO, cuckoo clusters, and dragonfly clusters) and transformation-based techniques (Hilbert transform and nonlinear regression) were used in this research. Twelve different classifiers, such as PCA, EM, logistic regression, GMM, BLDC, firefly clusters, harmonic search, detrend fluctuation analysis, PAC Bayesian learning, KNN-PAC Bayesian, softmax discriminant classifier, and detrend with SDC were utilized to detect CVD from dimensionally reduced PPG signals. The performance of the classifiers was assessed based on their metrics, such as accuracy, performance index, error rate, and a good detection rate. The Hilbert transform techniques with the harmonic search classifier outperformed all other classifiers, with an accuracy of 98.31% and a good detection rate of 96.55%.

A Review of Skin-Wearable Sensors for Non-Invasive Health Monitoring Applications

The early detection of fatal diseases is crucial for medical diagnostics and treatment, both of which benefit the individual and society. Portable devices, such as thermometers and blood pressure monitors, and large instruments, such as computed tomography (CT) and X-ray scanners, have already been implemented to collect health-related information. However, collecting health information using conventional medical equipment at home or in a hospital can be inefficient and can potentially affect the timeliness of treatment. Therefore, on-time vital signal collection via healthcare monitoring has received increasing attention. As the largest organ of the human body, skin delivers significant signals reflecting our health condition; thus, receiving vital signals directly from the skin offers the opportunity for accessible and versatile non-invasive monitoring. In particular, emerging flexible and stretchable electronics demonstrate the capability of skin-like devices for on-time and continuous long-term health monitoring. Compared to traditional electronic devices, this type of device has better mechanical properties, such as skin conformal attachment, and maintains compatible detectability. This review divides the health information that can be obtained from skin using the sensor aspect's input energy forms into five categories: thermoelectrical signals, neural electrical signals, photoelectrical signals, electrochemical signals, and mechanical pressure signals. We then summarize current skin-wearable health monitoring devices and provide outlooks on future development.

Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.

Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression

Purpose

Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal.

Methods

Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices.

Results

Compared with the CA, the VA is closer to PW (r = 0.539, P < 0.001 to r = 0.589, P < 0.001 in men; r = 0.325, P < 0.001 to r = 0.400, P < 0.001 in women), IPA (r =  - 0.446, P < 0.001 to r =  - 0.534, P < 0.001 in men; r =  - 0.623, P < 0.001 to r =  - 0.660, P < 0.001 in women), RBA (r = 0.328, P < 0.001 to r = 0.371, P < 0.001 in women), AIx (r = 0.659, P < 0.001 to r = 0.738, P < 0.001 in men; r = 0.547, P < 0.001 to r = 0.573, P < 0.001 in women), DAI (r = 0.517, P < 0.001 to r = 0.532, P < 0.001 in men; r = 0.507, P < 0.001 to r = 0.570, P < 0.001 in women) and PTT (r = 0.526, P < 0.001 to r = 0.659, P < 0.001 in men; r = 0.577, P < 0.001 to r = 0.814, P < 0.001 in women).

Conclusion

The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.

Photoplethysmogram Analysis and Applications: An Integrative Review

Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

Cardiovascular waveforms - can we extract more from routine signals?

Cardiovascular waveforms such as blood pressure, ECG and photoplethysmography (PPG), are routinely acquired by specialised monitoring devices. Such devices include bedside monitors, wearables and radiotelemetry which sample at very high fidelity, yet most of this numerical data is disregarded and focus tends to reside on single point averages such as the maxima, minima, amplitude, rate and intervals. Whilst, these measures are undoubtedly of value, we may be missing important information by simplifying the complex waveform signal in this way. This Special Collection showcases recent advances in the appraisal of routine signals. Ultimately, such approaches and technologies may assist in improving the accuracy and sensitivity of detecting physiological change. This, in turn, may assist with identifying efficacy or safety signals for investigational new drugs or aidpatient diagnosis and management, supporting scientific and clinical decision making.

A Hybrid Approach for Screening Endothelial Dysfunction using Photoplethysmography and Digital Thermal Monitoring

Cardiovascular diseases(CVDs) are the world's leading cause of death. Endothelial Dysfunction is an early stage of cardiovascular diseases and can effectively be used to detect the presence of the CVDs, monitor its progress and investigate the effectiveness of the treatment given. This study proposes a reliable approach for the screening of endothelial dysfunction via machine learning, using features extracted from a combination of Plethysmography, Digital Thermal Monitoring, biological features (age and gender) and anthropometry (BMI and pulse pressure). This case control study includes 55 healthy subjects and 45 subjects with clinically verified CVDs. Following the feature engineering stage, the results were subjected to dimension reduction and 5-fold cross-validation where it was observed that models Logistic Regression and Linear Discriminant provided the highest accuracies of 84% and 81% respectively. We propose that this study can be used as an efficient guide for the non-invasive screening of endothelial dysfunction.

Symmetric projection attractor reconstruction: Embedding in higher dimensions

Symmetric Projection Attractor Reconstruction (SPAR) provides an intuitive visualization and simple quantification of the morphology and variability of approximately periodic signals. The original method takes a three-dimensional delay coordinate embedding of a signal and subsequently projects this phase space reconstruction to a two-dimensional image with threefold symmetry, providing a bounded visualization of the waveform. We present an extension of the original work to apply delay coordinate embedding in any dimension N≥3 while still deriving a two-dimensional output with some rotational symmetry property that provides a meaningful visualization of the higher dimensional attractor. A generalized result is developed for taking N≥3 delay coordinates from a continuous periodic signal, where we determine invariant subspaces of the phase space that provide a two-dimensional projection with the required rotational symmetry. The result in each subspace is shown to be equivalent to following each pair of coefficients of the trigonometric interpolating polynomial of N evenly spaced points as the signal is translated horizontally. Bounds on the mean and the frequency response of our new coordinates are derived. We demonstrate how this aids our understanding of the attractor properties and its relationship to the underlying waveform. Our generalized result is then extended to real, approximately periodic signals, where we demonstrate that the higher dimensional SPAR method provides information on subtle changes in different parts of the waveform morphology.

Remote Blood Pressure Monitoring With a Wearable Photoplethysmographic Device (Senbiosys): Protocol for a Single-Center Prospective Clinical Trial

BACKGROUND

Wearable devices can provide user-friendly, accurate, and continuous blood pressure (BP) monitoring to assess patients' vital signs and achieve remote patient management. Remote BP monitoring can substantially improve BP control. The newest cuffless BP monitoring devices have emerged in patient care using photoplethysmography.

OBJECTIVE

The Senbiosys trial aims to compare BP measurements of a new device capturing a photoplethysmography signal on the finger versus invasive measurements performed in patients with an arterial catheter in the intensive care unit (ICU) or referred for a coronarography at the Hospital of Fribourg.

METHODS

The Senbiosys study is a single-center, single-arm, prospective trial. The study population consists of adult patients undergoing coronarography or patients in the ICU with an arterial catheter in place. This study will enroll 35 adult patients, including 25 patients addressed for a coronarography and 10 patients in the ICU. The primary outcome is the assessment of mean bias (95% CI) for systolic BP, diastolic BP, and mean BP between noninvasive and invasive BP measurements. Secondary outcomes include a reliability index (Qualification Index) for BP epochs and count of qualified epochs.

RESULTS

Patient recruitment started in June 2021. Results are expected to be published by December 2021.

CONCLUSIONS

The findings of the Senbiosys trial are expected to improve remote BP monitoring. The diagnosis and treatment of hypertension should benefit from these advancements.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04379986; https://clinicaltrials.gov/ct2/show/NCT04379986.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/30051.

Assessing cognitive load in adolescent and adult students using photoplethysmogram morphometrics

Compared to cardiac parameters and skin conductivities, the photoplethysmogram (PPG) recorded at fingertips and other parts near to peripheral nerve ends have been recently revealed to be yet another sensitive measure for cognitive load assessment. However, there is so far no research on measuring adolescents' cognitive load using physiological signals. A comprehensive study on the effects of PPG morphometrics over a cohort covering both adolescent and adult students is also absent. In this study, we analyze the morphological features of PPG on cognitive load assessment and compare them between adolescent and adult students. Experiments on two-level arithmetic tasks show that the PPG morphometrics reached the same level of significance on the effect of task difficulty/period as heart rate, and different morphological behaviors were also shown between adolescent and adult students during the cognitive task effects, which may imply their physiological differences across age. Physiological signals recorded by wearable devices are also found to be effective in measuring cognitive load.

Pulse decomposition analysis in photoplethysmography imaging

OBJECTIVE

Photoplethysmography imaging (PPGI) has gained immense attention over the last few years but only a few works have addressed morphological analysis so far. Pulse wave decomposition (PWD), i.e. the decomposition of a pulse wave by a varying number of kernels, allows for such analyses. This work investigates the applicability of PWD algorithms in the context of PPGI.

APPROACH

We used simulated and experimental data to compare various PWD algorithms from the literature regarding their robustness against noise and motion artifacts while preserving morphological information as well as regarding their ability to reveal physiological changes by PPGI.

MAIN RESULTS

Our experiments prove that algorithms that combine Gamma and Gaussian distributions outperform other choices. Further, algorithms with two kernels exhibit the highest robustness against noise and motion artifacts (improvement in [Formula: see text] of 14.09 %) while preserving the morphology similarly to algorithms using more kernels. Lastly, we showed that PWD can reveal physiological changes upon distal stimuli by PPGI.

SIGNIFICANCE

This work proves the feasibility of pulse decomposition analysis in PPGI, particularly for algorithms with a low number of kernels, and opens up novel applications for PPGI. Not only for PPGI but for future research on PWD in general, our findings have importance as they elucidate differences between PWD algorithms and emphasize the importance of using initial values. To support such future research, we have released the algorithms and simulated data to the public.

A digital biomarker of diabetes from smartphone-based vascular signals

The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.

Capabilities of photoplethysmography as a method for screening of cardiovascular system pathology

Currently, vegetative dysfunction considered to be one of principal mechanisms in the pathogenesis of cardiovascular pathology, which causes a cascade of events leading to changes in the properties and a structure of vascular wall. This review article contains literature from various databases (Russian science citation index, PubMed, Google Shcolar, Scopus). It presents the methods for assessing vegetative imbalance. In particular, the method of photoplethysmography (PPGV) is considered for recording periodic fluctuations at various frequencies in the distal vascular bed which characterize physiological processes (cardiac activity, respiratory influences, neurogenic, myogenic and endothelial activity). In addition, other diagnostic capabilities of PPGV such as heart rate (HR) assessment, determining the properties of vascular wall and the level of blood saturation are elucidated. This paper demonstrates a wide range of PPGV applications. The simplicity of PPGV reproduction and its cost-effectiveness make it feasible both in routine clinical practice for the purposes of screening for cardiovascular pathology, and for individual health monitoring incorporated in smart devices.

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders

The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine-Radial Basis Function (SVM-RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.

Screening Test on Metabolic Syndrome Using Electro Interstitial Scan Instrument

Background

Metabolic syndrome is diagnosed using clinical and laboratory data. Electro interstitial scan (EIS) is a rapid and noninvasive screening. It measures and calculates the parameters to reflect hypertension, sympathetic activity, stiffness of the arteries, body fat composition, leptin and insulin resistance. Metabolic syndrome will be diagnosed if calculated score ≥10 CU.

Purpose

To evaluate the accuracy, validity and appropriate cut-off score to diagnose metabolic syndrome.

Materials and Methods

A cross-sectional study was conducted using the population-based approach. Metabolic syndrome was diagnosed according to the modified National Cholesterol Education Program (NCEP ATP III) and International Diabetes Federation (IDF) criteria. The appropriate cut-off score to diagnose metabolic syndrome by instrument was determined.

Results

A total of 253 participants were enrolled with mean age of 40.06±6.33 years, and 64.43% (163/253) were female. Metabolic syndrome was diagnosed among 123 (48.62%) and 104 (41.11%) patients according to the NCEP ATP III and IDF criteria, respectively. The diagnostic indices of metabolic syndrome score ≥10 CU had a sensitivity of 23.6% and 27.9%, a specificity of 100% and area under ROC of 0.62 and 0.64, according to the NCEP ATP III and IDF criteria, respectively. The best cut-off level of metabolic syndrome score was ≥9 CU with a sensitivity of 79.67% (95% CI, 71.5–86.4%) and 88.46% (95% CI, 80.7–93.9%), a specificity of 96.92% (95% CI, 92.3–99.2%) and 93.29% (95% CI, 88.0–96.7%) and area under ROC 0.89 (95% CI, 0.85–0.93) and 0.92 (95% CI, 0.88–0.95), respectively, according to the NCEP ATP III and IDF criteria.

Conclusion

Screening of metabolic syndrome using ES TECK in a Thai population demonstrated inadequate accuracy when using metabolic syndrome score ≥10 CU. We recommend using a metabolic syndrome score ≥9 CU to provide the best accuracy. This instrument is safe, fast and easy to use for metabolic syndrome screening.

Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

Is Heart Rate a Confounding Factor for Photoplethysmography Markers? A Systematic Review

Finger photoplethysmography (PPG) waveform is blood volume change of finger microcirculation that reflects vascular function. Reflection index (RI), stiffness index (SI) and second derivative of photoplethysmogram (SDPPG) are derived from PPG waveforms proposed as cardiovascular disease (CVD) markers. Heart rate (HR) is a known factor that affects vascular function. Individual resting HR variation may affect RI, SI and SDPPG. This review aims to identify studies about the relationship between HR with RI, SI and SDPPG among humans. A literature search was conducted in Medline via the Ebscohost and Scopus databases to find relevant articles published within 11 years. The main inclusion criteria were articles in the English language that discuss the relationship between HR with RI, SI and SDPPG using PPG among humans. The search found 1960 relevant articles but only six articles that met the inclusion criteria. SI and RI showed an association with HR. SDPPG (SDPPG-b/SDPPG-a ratio, SDPPG-d/SDPPG-a ratio, aging index (AGI) and revised aging index (RAGI)) also had an association with HR. Only RI had a considerable association with HR, the association between SI and HR was non-considerable and the association between HR and SDPPG was inconclusive. Further interventional studies should be conducted to investigate this issue, as a variation in resting HR may challenge the validity of PPG-based CVD markers.

Association of the second derivative of photoplethysmogram with age, hemodynamic, autonomic, adiposity, and emotional factors

The second derivative of photoplethysmogram (SDPTG) is used as an arterial stiffness marker. This study aimed to examine the associations between SDPTG indices and age, in addition to hemodynamic, autonomic, adiposity, and emotional factors.This study had a cross-sectional chart review design, and electronic medical records of 262 women outpatients (mean ± SD,|38.57| ± |11.64 years) were reviewed. Among SDPTG measurements, b/a, c/a, d/a, and (b-c-d)/a were considered. Hemodynamic measurements included systolic and diastolic blood pressure (SBP and DBP) and cardiac output. Autonomic measurements included low and high frequency (LF and HF) values of the heart rate variability. Adiposity measurements included body mass index (BMI) and waist-hip ratio (WHR). Tension, anger, depression, fatigue, confusion, and vigor scores using the Profile of the Mood States were included as emotional markers. All data were normalized through the Box-Cox transformation, and 4 hierarchical regression models were constructed.Age was independently predictive of SDPTG, hemodynamic, autonomic, and adiposity factors (β; 0.143-0.648).After the adjustment for age, SBP and DBP showed negative correlations with d/a (r = -0.201, -0.262), whereas BMI, WHR, LF, and HF showed positive correlations with c/a (r = 0.126, 0.131, 0.151, 0.234). In the hierarchical regression modeling, age and hemodynamic factors were directly predictive of SDPTG indices (β; 0.103-0.626). Age had moderating effects between diastolic blood pressure, heart rate, depression scores, and SDPTG indices (β; 0.104-0.176).In conclusion, age, hemodynamic, adiposity, and autonomic factors may be independently associated with SDPTG indices for women. As age has moderating effects between hemodynamic, emotional factors, and SDPTG indices, its moderating effects should be considered when assessing arterial stiffness using SDPTG indices.

The use of photoplethysmography for assessing hypertension

The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.

EM-Wave Biosensors: A Review of RF, Microwave, mm-Wave and Optical Sensing

This article presents a broad review on optical, radio-frequency (RF), microwave (MW), millimeter wave (mmW) and terahertz (THz) biosensors. Biomatter-wave interaction modalities are considered over a wide range of frequencies and applications such as detection of cancer biomarkers, biotin, neurotransmitters and heart rate are presented in detail. By treating biological tissue as a dielectric substance, having a unique dielectric signature, it can be characterized by frequency dependent parameters such as permittivity and conductivity. By observing the unique permittivity spectrum, cancerous cells can be distinguished from healthy ones or by measuring the changes in permittivity, concentration of medically relevant biomolecules such as glucose, neurotransmitters, vitamins and proteins, ailments and abnormalities can be detected. In case of optical biosensors, any change in permittivity is transduced to a change in optical properties such as photoluminescence, interference pattern, reflection intensity and reflection angle through techniques like quantum dots, interferometry, surface enhanced raman scattering or surface plasmon resonance. Conversely, in case of RF, MW, mmW and THz biosensors, capacitive sensing is most commonly employed where changes in permittivity are reflected as changes in capacitance, through components like interdigitated electrodes, resonators and microstrip structures. In this paper, interactions of EM waves with biomatter are considered, with an emphasis on a clear demarcation of various modalities, their underlying principles and applications.

Use of photoplethysmography to predict mortality in intensive care units

PURPOSE

The aim of this study was to evaluate and compare the capacity to predict hemodynamic variables obtained with photoplethysmography (PPG) and Acute Physiology and Chronic Health Evaluation (APACHE II) in patients hospitalized in the intensive care unit (ICU).

MATERIALS AND METHODS

A prospective cohort study was conducted in the adult ICU of Hospital Nossa Senhora da Conceição, located in Tubarão, Santa Catarina, Brazil. The data collected included the diagnosis for hospitalization, age, gender, clinical or surgical profile, PPG pulse curve signal, and APACHE II score in the first 24 hours. A bivariate and a multivariate logistic regressions were performed, with death as an outcome. A mortality model using artificial neural networks (ANNs) was proposed.

RESULTS

A total of 190 individuals were evaluated. Most of them were males (6:5), with a median age of 67 (54-75) years, and the main reasons for hospitalization were cardiovascular and neurological causes; half of them were surgical cases. APACHE II median score was 14 (8-19), with a median length of stay of 6 (3-15) days, and 28.4% of the patients died. The following factors were associated with mortality: age (OR=1.023; 95% CI 1.001-1.044; P=0.039), clinical profile (OR=5.481; 95% CI 2.646-11.354; P<0.001), APACHE II (OR=1.168; 95% CI 1.106-1.234; P<0.001), heart rate in the first 24 hours (OR=1.020; 95% CI 1.001-1.039; P=0.036), and time between the systolic and diastolic peak (∆T) intervals obtained with PPG (OR=0.989; 95% CI 0.979-0.998; P=0.015). Compared with the accuracy (area under the receiver-operating characteristic curve) 0.780 of APACHE II (95% CI 0.711-0.849; P<0.001), the multivariate logistic model showed a larger area of 0.858 (95% CI 0.803-0.914; P<0.001). In the model using ANNs, the accuracy was 0.895 (95% CI 0.851-0.940; P<0.001).

CONCLUSION

The mortality models using variables obtained with PPG, with the inclusion of epidemiological parameters, are very accurate and, if associated to APACHE II, improve prognostic accuracy. The use of ANN was even more accurate, indicating that this tool is important to help in the clinical judgment of the intensivist.

A review on wearable photoplethysmography sensors and their potential future applications in health care

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal’s second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

Associations between Job Strain and Arterial Stiffness: A Large Survey among Enterprise Employees from Thailand

As an intermediate endpoint to cardiovascular disease, arterial stiffness has received much attention recently. So far, the research on work stress and arterial stiffness is still sparse and inconsistent, and no investigations on work stress and cardiovascular health among the Thai working population have been reported. Therefore, we conducted an epidemiological study among 2141 Thai enterprise employees (858 men and 1283 women) who were free from any diagnosed cardiovascular disease. Work stress was measured using Karasek’s Job Demand–Control model for job strain (a combination of high demand and low control). Arterial stiffness was evaluated by a non-invasive approach using pulse-wave analysis based on a finger photoplethysmogram. Multivariable linear regression was applied to examine associations between job strain and arterial stiffness. In men, job strain was significantly associated with arterial stiffness (β  =  0.078, 95% confidence interval  =  0.026 to 0.130), after accounting for sociodemographic, behavioral, dietary and biomedical factors. However, the association in women was not significant. As the first study in Thailand on work stress and cardiovascular risk, we found that job strain might be an important risk factor for cardiovascular disease among Thai working men. Further studies with longitudinal design are warranted.

Utilization of second derivative photoplethysmographic features for myocardial infarction classification

Myocardial infarction (MI) is a common disease that causes morbidity and mortality. The current tools for diagnosing this disease are improving, but still have some limitations. This study utilised the second derivative of photoplethysmography (SDPPG) features to distinguish MI patients from healthy control subjects. The features include amplitude-derived SDPPG features (pulse height, ratio, jerk) and interval-derived SDPPG features (intervals and relative crest time (RCT)). We evaluated 32 MI patients at Pusat Perubatan Universiti Kebangsaan Malaysia and 32 control subjects (all ages 37-87 years). Statistical analysis revealed that the mean amplitude-derived SDPPG features were higher in MI patients than in control subjects. In contrast, the mean interval-derived SDPPG features were lower in MI patients than in the controls. The classifier model of binary logistic regression (Model 7), showed that the combination of SDPPG features that include the pulse height (d-wave), the intervals of "ab", "ad", "bc", "bd", and "be", and the RCT of "ad/aa" could be used to classify MI patients with 90.6% accuracy, 93.9% sensitivity and 87.5% specificity at a cut-off value of 0.5 compared with the single features model.

Association between uterine artery Doppler blood flow changes and arterial wall elasticity in pregnant women

INTRODUCTION

Uterine artery (UtA) Doppler velocimetry changes and increased arterial stiffness are associated with preeclampsia. We aimed to investigate the relation between UtA velocimetry changes and arterial stiffness in pregnant women.

METHODS

Doppler velocimetry and photoplethysmographic digital pulse wave analysis (DPA) were performed in 173 pregnant women in the second or the third trimester, where UtA Doppler pulsatility index (PI), diastolic notching, and UtA score (UAS) combining notching and high PI were calculated. DPA stiffness parameters representing large arteries were ejection elasticity index (EEI) and b/a, small arteries dicrotic index (DI) and d/a, and global stiffness the aging index (AI).

RESULTS

One hundred and thirty women had normal Doppler and 43 had diastolic notching, of whom nine had high PI. DI indicated increased stiffness in small arteries when notching was present (p = 0.044) and showed a significant but weak correlation to UAS (p = 0.025, tau 0.12). EEI and b/a indicated increased large artery stiffness (p ≤0.014), d/a small artery stiffness (p = 0.023), and AI a systemic stiffness (p = 0.040) when high PI.

CONCLUSION

High UtA PI was associated with increased systemic arterial stiffness, whereas notching was related to increased stiffness in small arteries only. This indicates pathophysiological differences between the two Doppler parameters.

Second derivative of the finger photoplethysmogram and cardiovascular mortality in middle-aged and elderly Japanese women

The second derivative of the digital photoplethysmogram (SDPTG) is an indicator of arterial stiffness. The ratio of the height of the d wave to the a wave of the SDPTG (d/a) is associated with functional peripheral vascular tension and represents aortic-blood pressure (BP) augmented by reflection waves from the periphery. This longitudinal study aimed to investigate the relationship between SDPTG and cardiovascular mortality in middle-aged and elderly Japanese women. From 1998 to 2008, we recruited 4373 women (50-79 years old at baseline) who underwent medical check-ups and SDPTG measurement. The SDPTG index (d/a) was calculated from the wave component height, and was divided into quartiles (Q) according to the d/a value. The median follow-up period was 9.0 years. The d/a value was negatively associated with age and BP, and positively associated with heart rate and body height. Using the Cox proportional hazards model, the hazard ratios for cardiovascular mortality for Q2, Q3 and Q4 were significantly higher than that of Q1. In multivariate analysis, the hazard ratio was 2.30 for Q3 (95% confidence interval (CI): 1.06-4.99, P<0.05) and 2.60 for Q4 (95% CI: 1.21-5.60, P<0.05), after adjustment for age, height, body mass index, BP levels, heart rate and other atherosclerosis-related factors. The hazard ratios of cardiovascular mortality for Q3 and Q4 were significantly higher compared with the reference (Q1). Thus, the SDPTG d/a is an independent predictor of cardiovascular mortality in middle-aged and elderly Japanese women.

Usefulness of the second derivative of the finger photoplethysmogram for assessment of end-organ damage: the J-SHIPP study

Early detection of pathological changes in the vasculature is required to identify individuals at risk of cardiovascular diseases. Noninvasive measurement of the second derivative of photoplethysmogram (SDPTG) might aid in evaluating vascular aging. Here we clarified the diagnostic significance of four SDPTG indices for end-organ damage. A total of 1613 community residents (65±10 years) were enrolled. Changes in blood flow volume at the forefinger were measured by photoplethysmography. SDPTG was computationally calculated from the plethysmogram, and the height of five peaks (a-e) on the SDPTG was measured. Carotid intima-media thickness (IMT), brachial-to-ankle pulse wave velocity (baPWV) and silent cerebral lesions were used as indices of end-organ damage. Multivariate analysis identified age, sex, systolic blood pressure and heart rate as strong determinants for the evaluated SDPTG indices, namely b/a, d/a and aging index ([b-d-c-e]/a). In addition, poor glycemic control and carotid IMT were also weakly associated with the SDPTG indices. Compared with other established risk factors, however, the association between the SDPTG indices and carotid IMT was weak or insignificant (b/a: β=0.069, P=0.002; d/a: β=-0.009, P=0.669; and aging index: β=0.047, P=0.037). Further, no significant association was noted between the SDPTG indices and silent lacunar infarction (b/a: P=0.111; d/a: P=0.263; and aging index: P=0.167) and periventricular hyperintensity (b/a: P=0.587; d/a: P=0.254; and aging index: P=0.429). Although the SDPTG indices evaluated here might represent structural and functional changes in arteries, they exhibited limited diagnostic significance for pathophysiological changes in large arteries, as well as small vessel diseases of the brain.

Digital Photoplethysmography for Assessment of Arterial Stiffness: Repeatability and Comparison with Applanation Tonometry

Introduction

Arterial stiffness is an independent risk factor for cardiovascular morbidity and can be assessed by applanation tonometry by measuring pulse wave velocity (PWV) and augmentation index (AIX) by pressure pulse wave analysis (PWA). As an inexpensive and operator independent alternative, photoelectric plethysmography (PPG) has been introduced with analysis of the digital volume pulse wave (DPA) and its second derivatives of wave reflections.

Objective

The objective was to investigate the repeatability of arterial stiffness parameters measured by digital pulse wave analysis (DPA) and the associations to applanation tonometry parameters.

Methods and Results

112 pregnant and non-pregnant individuals of different ages and genders were examined with SphygmoCor arterial wall tonometry and Meridian DPA finger photoplethysmography. Coefficients of repeatability, Bland-Altman plots, intraclass correlation coefficients and correlations to heart rate (HR) and body height were calculated for DPA variables, and the DPA variables were compared to tonometry variables left ventricular ejection time (LVET), PWV and AIX. No DPA variable showed any systematic measurement error or excellent repeatability, but dicrotic index (DI), dicrotic dilatation index (DDI), cardiac ejection elasticity index (EEI), aging index (AI) and second derivatives of the crude pulse wave curve, b/a and e/a, showed good repeatability. Overall, the correlations to AIX were better than to PWV, with correlations coefficients >0.70 for EEI, AI and b/a. Considering the level of repeatability and the correlations to tonometry, the overall best DPA parameters were EEI, AI and b/a. The two pansystolic time parameters, ejection time compensated (ETc) by DPA and LVET by tonometry, showed a significant but weak correlation.

Conclusion

For estimation of the LV function, ETc, EEI and b/a are suitable, for large artery stiffness EEI, and for small arteries DI and DDI. The only global parameter, AI, showed a high repeatability and the overall best correlations with AIX and PWV.

A novel feature ranking algorithm for biometric recognition with PPG signals

This study is intended for describing the application of the Photoplethysmography (PPG) signal and the time domain features acquired from its first and second derivatives for biometric identification. For this purpose, a sum of 40 features has been extracted and a feature-ranking algorithm is proposed. This proposed algorithm calculates the contribution of each feature to biometric recognition and collocates the features, the contribution of which is from great to small. While identifying the contribution of the features, the Euclidean distance and absolute distance formulas are used. The efficiency of the proposed algorithms is demonstrated by the results of the k-NN (k-nearest neighbor) classifier applications of the features. During application, each 15-period-PPG signal belonging to two different durations from each of the thirty healthy subjects were used with a PPG data acquisition card. The first PPG signals recorded from the subjects were evaluated as the 1st configuration; the PPG signals recorded later at a different time as the 2nd configuration and the combination of both were evaluated as the 3rd configuration. When the results were evaluated for the k-NN classifier model created along with the proposed algorithm, an identification of 90.44% for the 1st configuration, 94.44% for the 2nd configuration, and 87.22% for the 3rd configuration has successfully been attained. The obtained results showed that both the proposed algorithm and the biometric identification model based on this developed PPG signal are very promising for contactless recognizing the people with the proposed method.

Associations between the autonomic nervous system and the second derivative of the finger photoplethysmogram indices

AIM

The indices of the second derivative of the finger photoplethysmogram(SDPTG) denote stiffness of large arteries, peripheral vascular resistance and vascular aging. However, the association between the autonomic nervous activity and the SDPTG indices has not yet been elucidated.

METHODS

The SDPTG and heart rate variability(HRV) were consecutively measured in the sitting position on the day before surgery in 168 patients 18-89 years of age. The relationships between the SDPTG indices(b/a, c/a, d/a and e/a) and HRV indices(power spectral analysis and time domain analysis parameters) were analyzed. The relationships between c/a and atherosclerosis-based conditions and risk factors for atherosclerosis were also evaluated.

RESULTS

The SDPTG index b/a was negatively associated and the d/a index was positively associated with the low-frequency(LF)(R=-0.44 and 0.42, respectively) and high-frequency(HF) components(R=-0.31 and 0.35, respectively). The SDPTG index c/a was also positively associated with the LF(R=0.40) and HF(R=0.44) components. A multivariate regression analysis showed that the LF, HF and heart rate were independent determinants of the c/a. Furthermore, the c/a values were significantly lower in the patients with hypertension, diabetes mellitus and hyperlipidemia than in those without these diseases, and a reduced c/a was significantly associated with increased serum triglyceride and total cholesterol concentrations.

CONCLUSIONS

These findings suggest that a decrease in c/a is associated with a reduced baroreflex response of the peripheral vasomotor activity and a decreased cardiac parasympathetic activity. Furthermore, a decrease in c/a was found to be associated with atherosclerosis-based conditions, such as hypertension, diabetes mellitus and hyperlipidemia.

Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions

Photoplethysmogram (PPG) monitoring is not only essential for critically ill patients in hospitals or at home, but also for those undergoing exercise testing. However, processing PPG signals measured after exercise is challenging, especially if the environment is hot and humid. In this paper, we propose a novel algorithm that can detect systolic peaks under challenging conditions, as in the case of emergency responders in tropical conditions. Accurate systolic-peak detection is an important first step for the analysis of heart rate variability. Algorithms based on local maxima-minima, first-derivative, and slope sum are evaluated, and a new algorithm is introduced to improve the detection rate. With 40 healthy subjects, the new algorithm demonstrates the highest overall detection accuracy (99.84% sensitivity, 99.89% positive predictivity). Existing algorithms, such as Billauer's, Li's and Zong's, have comparable although lower accuracy. However, the proposed algorithm presents an advantage for real-time applications by avoiding human intervention in threshold determination. For best performance, we show that a combination of two event-related moving averages with an offset threshold has an advantage in detecting systolic peaks, even in heat-stressed PPG signals.

New photoplethysmographic signal analysis algorithm for arterial stiffness estimation

The ability to identify premature arterial stiffening is of considerable value in the prevention of cardiovascular diseases. The “ageing index” (AGI), which is calculated from the second derivative photoplethysmographic (SDPPG) waveform, has been used as one method for arterial stiffness estimation and the evaluation of cardiovascular ageing. In this study, the new SDPPG analysis algorithm is proposed with optimal filtering and signal normalization in time. The filter parameters were optimized in order to achieve the minimal standard deviation of AGI, which gives more effective differentiation between the levels of arterial stiffness. As a result, the optimal low-pass filter edge frequency of 6 Hz and transitionband of 1 Hz were found, which facilitates AGI calculation with a standard deviation of 0.06. The study was carried out on 21 healthy subjects and 20 diabetes patients. The linear relationship (r = 0.91) between each subject's age and AGI was found, and a linear model with regression line was constructed. For diabetes patients, the mean AGI value difference from the proposed model y_AGI was found to be 0.359. The difference was found between healthy and diabetes patients groups with significance level of P < 0.0005.